MonteQuest wrote:tsakach wrote: Can you reference the original data sources used to produce these graphs?
The first one came from the EIA. The second is obviously an extrapolation of the first.
Did the EIA data come from the following:
2001 National Household Travel Survey conducted by the U.S. Department of Transportation and augmented by EIA
The first graph seems to make sense, especially if you look at the EIA data:
U.S. Average Vehicle-Miles Traveled by Vehicle Fuel Economy Category, 2001.
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Vehicle fuel economy category (mpg) <10.9 11-12.9 13-15.9 16-18.9 19-21.9 22-24.9 25-25.9 >30
Average miles traveled (thousands) 2.4 3.1 8.5 11.5 12.4 14.8 15.7 17.0
If you calculate the total gallons consumed per year by vehicle category, you have:
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Vehicle fuel economy category (mpg) <10.9 11-12.9 13-15.9 16-18.9 19-21.9 22-24.9 25-25.9 >30
Average miles traveled (thousands) 2.4 3.1 8.5 11.5 12.4 14.8 15.7 17.0
Average gallons consumed 220 240 534 608 566 594 606 566
This seems to be fairly consistent with the data you provided in the first graph. In fact, I find the data in these tables to be more compelling evidence of the rebound effect. Here you can see a clear relation between fuel efficiency and miles traveled. Since the data occurs within the same year, variables such as population growth or fuel prices are factored out.
The average gallons consumed is derived from fuel efficiency and total miles traveled. If the gallons consumed also showed a linear increase that tracked fuel efficiency like the miles traveled data, it would certainly seem to suggest that something like Jevons Paradox is occuring. The very low efficiency vehicles (<13 mpg) travel fewer miles, while the remaining vehicle efficiency categories do not reflect an obvious increase or decrease in fuel consumption based on efficiency. It is certainly possible that a 7-8% overall decrease in fuel consumption may occur, even with increased annual mileage as stated in Greene, Kahn and Gibson, 1999. But there is no indication here that an increase in efficiency causes an increase in consumption, only an increase in miles traveled is apparent.
The second graph appears to draw a connection between an increase in efficiency and an increase in gasoline consumption. But with so many other variables involved over the time period, such as population growth or gasoline prices, it is impossible to draw a connection with the data provided.
The third graph indicates that miles traveled increased by a factor of 5x over population growth. But again, this could be caused by other time-related factors such as gasoline prices, people moving further away from work, etc.
The data provided in the first graph shows evidence of the rebound effect on miles traveled, but the other graphs do not provide evidence that Jevons Paradox is the cause of increased consumption. As mentioned previously, there are many variables involved making it very difficult to isolate the causes of increased or decreased consumption.
While the rebound effect is acknowledged as a real phenomenon and is well studied, the extent of the rebound is a subject of debate. Jevons Paradox on the other hand seems to be dissmissed as something that rarely occurs and only under special circumstances. I would argue that Jevons Paradox is a special case of the rebound effect, where the extent of the rebound is such that the resulting consumption is greater than the gain in efficiency, resulting in an overall increase in demand. But in most cases, gains in efficiency result in an overall reduction of demand, but the reduction in demand is less than the gain in efficiency due to the rebound.